paper | Rafael A Calvo

M. Liu, R.A. Calvo, V. Rus (2014) “Automatic Generation and Ranking of Questions for Critical Review”. Educational Technology & Society. Volume 17, Issue 2, 2014.
Critical review skill is one important aspect of academic writing. Generic trigger questions have been widely used to support this activity. When students have a concrete topic in mind, trigger questions are less effective if they are too general. This article presents a learning-to-rank based system which automatically generates specific trigger questions from citations for critical review support. The performance of the proposed question ranking models was evaluated and the quality of generated questions is reported. Experimental results showed an accuracy of 75.8% on the top 25% ranked questions. These top ranked questions are as useful for self-reflection as questions generated by human tutors and supervisors. A qualitative analysis was also conducted using an information seeking question taxonomy in order to further analyze the questions generated by humans. The analysis revealed that explanation and association questions are the most frequent question types and that the explanation questions are considered the most valuables by student writers.

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Our paper:

H. Monkaresi, R. A. Calvo, H. Yan, (To appear) “A Machine Learning Approach to Improve Contactless Heart Rate Monitoring Using a Webcam”, IEEE Journal of Biomedical and Health Informatics (J-BHI)

has been accepted for publication. You can read the complete PrePublication PDF

Unobtrusive, contactless recordings of physiological signals is very important for many health and human-computer interaction applications. Most current systems require sensors which intrusively touch the user’s skin. Recent advances in contact-free physiological signals open the door to many new types of applications. This technology promises to measure heart rate (HR) and respiration using video only.